English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper addresses the issues of controlling and analyzing the populationdiversity in quantum-behaved particle swarm optimization (QPSO), which is anoptimization approach motivated by concepts in quantum mechanics and PSO. Inorder to gain an in-depth understanding of the role the diversity plays in theevolving process, we first define the genotype diversity by the distance to theaverage point of the particles' positions and the phenotype diversity by thefitness values for the QPSO. Then, the correlations between the two types ofdiversities and the search performance are tested and analyzed on severalbenchmark functions, and the distance-to-average-point diversity is showed tohave stronger association with the search performance during the evolvingprocesses. Finally, in the light of the performed diversity analyses, twostrategies for controlling the distance-to-average-point diversities areproposed for the purpose of improving the search ability of the QPSO algorithm.Empirical studies on the QPSO with the introduced diversity control methods areperformed on a set of benchmark functions from the CEC 2005 benchmark suite.The performance of the proposed methods are evaluated and compared with theoriginal QPSO and other PSO variants.

Analyzing and controlling diversity in quantum-behaved particle swarm optimization